Run black on python folder

python/elastic_search_serialization.py

@@ -226,7 +226,10 @@ def elastic_search_parallel_bulk(
 
 @mgp.read_proc
 def connect(
-    elastic_url: str, ca_certs: Union[str, None]=None, elastic_user: str="", elastic_password: str=""
+    elastic_url: str,
+    ca_certs: Union[str, None] = None,
+    elastic_user: str = "",
+    elastic_password: str = "",
 ) -> mgp.Record(connection_status=mgp.Map):
     """Establishes connection with the Elasticsearch. This configuration needs to be specific to the Elasticsearch deployment. Uses basic authentication
     Args:

python/graph_analyzer.py

@@ -145,7 +145,6 @@ def _get_analysis_funcs():
 def _analyze_graph(
     context: mgp.ProcCtx, g: nx.MultiDiGraph, analyses: List[str]
 ) -> List[Tuple[str, str]]:
-
     functions = (
         _get_analysis_funcs()
         if analyses is None

python/igraphalg.py

@@ -22,7 +22,6 @@ def maxflow(
     target: mgp.Vertex,
     capacity: str = "weight",
 ) -> mgp.Record(max_flow=mgp.Number):
-
     graph = MemgraphIgraph(ctx=ctx, directed=True)
     max_flow_value = graph.maxflow(source=source, target=target, capacity=capacity)
 
@@ -95,7 +94,6 @@ def mincut(
 def topological_sort(
     ctx: mgp.ProcCtx, mode: str = "out"
 ) -> mgp.Record(nodes=mgp.List[mgp.Vertex]):
-
     if mode not in [
         TopologicalSortingModes.IN.value,
         TopologicalSortingModes.OUT.value,

python/link_prediction.py

@@ -644,7 +644,6 @@ def recommend(  # noqa: C901
 def get_training_results(
     ctx: mgp.ProcCtx,
 ) -> mgp.Record(training_results=mgp.Any, validation_results=mgp.Any):
-
     """This method is used when user wants to get performance data obtained from the last training. It is in the form of list of records where each record is a Dict[metric_name, metric_value]. Training and validation
     results are returned.
 
@@ -1011,6 +1010,7 @@ def validate_user_parameters(parameters: mgp.Map) -> None:  # noqa: C901
     Returns:
         Nothing or raises an exception if something is wrong.
     """
+
     # Hacky Python
     def raise_(ex):
         raise ex

python/mage/graph_coloring_module/algorithms/meta_heuristics/quantum_annealing.py

@@ -121,7 +121,6 @@ def _markow_chain(
         index: int,
         parameters: Dict[str, Any],
     ) -> None:
-
         temperature = param_value(graph, parameters, Parameter.QA_TEMPERATURE)
         max_steps = param_value(graph, parameters, Parameter.QA_MAX_STEPS)
         mutation = param_value(graph, parameters, Parameter.MUTATION)

python/mage/graph_coloring_module/components/chain_chunk.py

@@ -66,7 +66,6 @@ def __init__(
         prev_indv: Individual,
         next_indv: Individual,
     ):
-
         super().__init__(graph, individuals)
         self._prev_indv = prev_indv
         self._next_indv = next_indv

python/mage/graph_coloring_module/components/individual.py

@@ -25,7 +25,6 @@ def __init__(
         conflict_nodes: Set[int] = None,
         conflicts_counter: List[int] = None,
     ):
-
         self._graph = graph
         self._no_of_units = len(graph)
         self._no_of_colors = no_of_colors
@@ -115,7 +114,7 @@ def replace_units(self, indices: List[int], colors: List[int]):
         conflict_nodes = self._conflict_nodes.copy()
         conflict_edges = self.conflicts_weight
 
-        for (index, color) in zip(indices, colors):
+        for index, color in zip(indices, colors):
             if not (0 <= color < self._no_of_colors):
                 raise IllegalColorException(
                     "The given color is not in the range of allowed colors!"
@@ -152,7 +151,6 @@ def _calculate_diff(
         conflicts_counter: List[int],
         conflict_nodes: Set[int],
     ) -> Tuple[int, List[int], Set[int]]:
-
         diff = 0
         for neigh, weight in self.graph.weighted_neighbors(node):
             if chromosome[node] == chromosome[neigh]:

python/mage/graph_coloring_module/components/population.py

@@ -10,7 +10,6 @@ class Population(ABC):
     information with individuals that are located next to it."""
 
     def __init__(self, graph: Graph, individuals: List[Individual]):
-
         self._size = len(individuals)
         self._individuals = individuals
         self._best_individuals = self._individuals[:]

python/mage/graph_coloring_module/iteration_callbacks/callback_actions/simple_tunneling.py

@@ -35,7 +35,6 @@ def execute(
         population: Population,
         parameters: Dict[str, Any] = None,
     ) -> None:
-
         simple_tunneling_max_attempts = param_value(
             graph, parameters, Parameter.SIMPLE_TUNNELING_MAX_ATTEMPTS
         )

python/mage/graph_coloring_module/utils/parameters_utils.py

@@ -6,7 +6,6 @@
 def param_value(
     graph: Graph, parameters: Dict[str, Any], param: str, initial_value: Any = None
 ) -> Any:
-
     if parameters is None:
         if initial_value is None:
             return None

python/mage/node2vec/second_order_random_walk.py

@@ -134,7 +134,6 @@ def calculate_edge_transition_probs(
         unnorm_trans_probs = []
 
         for dest_neighbor_id in graph.get_neighbors(dest_node_id):
-
             edge_weight = graph.get_edge_weight(dest_node_id, dest_neighbor_id)
 
             if dest_neighbor_id == src_node_id:
@@ -153,7 +152,7 @@ def set_graph_transition_probs(self, graph: Graph) -> None:
         Args:
             graph (Graph): Graph for which to set first pass transition probs
         """
-        for (node_from, node_to) in graph.get_edges():
+        for node_from, node_to in graph.get_edges():
             graph.set_edge_transition_probs(
                 (node_from, node_to),
                 self.calculate_edge_transition_probs(graph, node_from, node_to),

python/mage/node2vec_online_module/walk_sampling.py

@@ -67,7 +67,6 @@ def process_new_edge(self, source: Any, target: Any, time: int) -> List[List[Any
         return walks
 
     def sample_single_walk(self, source: Any, target: Any, time: int) -> List[Any]:
-
         node_ = source
         time_ = self.last_timestamp.get(source, 0)
         centrality_ = self.centrality.get(source, 0)
@@ -84,7 +83,7 @@ def sample_single_walk(self, source: Any, target: Any, time: int) -> List[Any]:
             sum_ = centrality_ * random.uniform(0, 1)
             sum__ = 0
             broken = False
-            for (n, t, c) in reversed(self.graph[node_]):
+            for n, t, c in reversed(self.graph[node_]):
                 if t < time_:
                     sum__ += (c + 1) * self.beta * math.exp(self.c * (t - time_))
                     if sum__ >= sum_:
@@ -100,7 +99,6 @@ def sample_single_walk(self, source: Any, target: Any, time: int) -> List[Any]:
         return (node_, target)
 
     def update(self, source: Any, target: Any, time: int) -> None:
-
         # all walks that terminated at the target before adding the new edge are decayed
         if target in self.centrality:
             self.centrality[target] *= math.exp(
@@ -133,7 +131,7 @@ def clean_in_edges(self, node: Any, current_time: int) -> None:
 
         # possible improvement by binary search
         index = 0
-        for (source, time, centrality) in self.graph[node]:
+        for source, time, centrality in self.graph[node]:
             if current_time - time < self.cutoff:
                 break
             index += 1

python/mage/node_classification/models/gatjk.py

@@ -42,7 +42,6 @@ def __init__(
         self.bns = torch.nn.ModuleList()
         self.bns.append(torch.nn.BatchNorm1d(hidden_features_size[0] * heads))
         for i in range(len(hidden_features_size) - 2):
-
             self.convs.append(
                 GATConv(
                     hidden_features_size[i] * heads,

python/mage/node_classification/utils/extract_from_database.py

@@ -38,7 +38,6 @@ def nodes_fetching(
     inv_label_reindexing = defaultdict()
 
     for node in nodes:
-
         if features_name not in node.properties:
             continue  # if features are not available, skip the node
 
@@ -79,7 +78,6 @@ def nodes_fetching(
 
     # since node_types is Counter, key is the node type and value is the number of nodes of that type
     for node_type, num_types_node in node_types.items():
-
         # for each node type, create a tensor of size num_types_node x embedding_lengths[node_type]
         data[node_type].x = torch.tensor(
             np.zeros((num_types_node, embedding_lengths[node_type])),

python/mage/tgn/definitions/tgn.py

@@ -179,7 +179,6 @@ def _process_current_batch(
         edge_idxs: np.array,
         timestamps: np.array,
     ) -> None:
-
         self._update_raw_message_store_current_batch(
             sources=sources,
             destinations=destinations,
@@ -203,7 +202,6 @@ def _process_current_batch(
             self.node_features[node_id] = node_feature
 
     def _process_previous_batches(self) -> None:
-
         # dict nodeid -> List[event]
         raw_messages = self.raw_message_store.get_messages()
 
@@ -226,7 +224,6 @@ def _update_raw_message_store_current_batch(
         edge_features: Dict[int, torch.Tensor],
         node_features: Dict[int, torch.Tensor],
     ) -> None:
-
         interaction_events: Dict[int, List[Event]] = create_interaction_events(
             sources=sources,
             destinations=destinations,
@@ -269,7 +266,6 @@ def _create_messages(
                         )
                     )
                 elif type(message) is InteractionRawMessage:
-
                     interaction_raw_message = message
 
                     processed_messages_dict[node].append(
@@ -424,8 +420,12 @@ def _form_computation_graph(
             cur_arr = [(n, v) for (n, v) in prev]
 
             node_arr = []
-            for (v, t) in cur_arr:
-                (neighbors, edge_idxs, timestamps,) = (
+            for v, t in cur_arr:
+                (
+                    neighbors,
+                    edge_idxs,
+                    timestamps,
+                ) = (
                     self.temporal_neighborhood.get_neighborhood(
                         v, t, self.num_neighbors
                     )
@@ -447,8 +447,12 @@ def _form_computation_graph(
 
         global_edge_indexes = []
         global_timestamps = []
-        for (v, t) in node_layers[0]:
-            (neighbors, edge_idxs, timestamps,) = (
+        for v, t in node_layers[0]:
+            (
+                neighbors,
+                edge_idxs,
+                timestamps,
+            ) = (
                 self.temporal_neighborhood.get_neighborhood(v, t, self.num_neighbors)
                 if (v, t) not in sampled_neighborhood
                 else sampled_neighborhood[(v, t)]

python/mgp_igraph.py

@@ -45,7 +45,6 @@ def pagerank(
     def get_all_simple_paths(
         self, v: mgp.Vertex, to: mgp.Vertex, cutoff: int
     ) -> List[List[mgp.Vertex]]:
-
         paths = [
             self._convert_vertex_ids_to_mgp_vertices(path)
             for path in super().get_all_simple_paths(
@@ -86,7 +85,6 @@ def community_leiden(
     def mincut(
         self, source: mgp.Vertex, target: mgp.Vertex, capacity: str
     ) -> Tuple[List[mgp.Vertex], float]:
-
         cut = super().mincut(
             source=self.id_mappings[source.id],
             target=self.id_mappings[target.id],
@@ -117,7 +115,6 @@ def spanning_tree(
     def shortest_path_length(
         self, source: mgp.Vertex, target: mgp.Vertex, weights: str
     ) -> float:
-
         length = super().distances(
             source=self.id_mappings[source.id],
             target=self.id_mappings[target.id],
@@ -134,7 +131,6 @@ def all_shortest_path_lengths(self, weights: str) -> List[List[float]]:
     def get_shortest_path(
         self, source: mgp.Vertex, target: mgp.Vertex, weights: str
     ) -> List[mgp.Vertex]:
-
         path = super().get_shortest_paths(
             v=self.id_mappings[source.id],
             to=self.id_mappings[target.id],
@@ -149,7 +145,6 @@ def get_vertex_by_id(self, id: int) -> mgp.Vertex:
     def _convert_vertex_ids_to_mgp_vertices(
         self, vertex_ids: List[int]
     ) -> List[mgp.Vertex]:
-
         vertices = []
         for id in vertex_ids:
             vertices.append(

python/node_classification.py

@@ -22,6 +22,7 @@
 # constants
 ##############################
 
+
 # parameters for the model
 class ModelParams:
     IN_CHANNELS = "in_channels"
@@ -597,8 +598,10 @@ def train(
 
 
 @mgp.read_proc
-def get_training_data() -> mgp.Record(
-    epoch=int, loss=float, val_loss=float, train_log=mgp.Any, val_log=mgp.Any
+def get_training_data() -> (
+    mgp.Record(
+        epoch=int, loss=float, val_loss=float, train_log=mgp.Any, val_log=mgp.Any
+    )
 ):
     """This function is used so user can see what is logged data from training.
 

python/tests/graph_coloring/test_conflict_error.py

@@ -73,7 +73,6 @@ def test_individual_error_no_setting(
 
 
 def test_population_error(set_seed, chain_population):
-
     error = ConflictError().population_err(
         graph,
         chain_population,

python/tests/node2vec/test_basic_graph.py

@@ -43,7 +43,6 @@
 
 @pytest.fixture(params=[True, False])
 def is_directed(request):
-
     return request.param
 
 

python/tests/node2vec_online/test_walk_sampling.py

@@ -26,7 +26,6 @@ def test_correct_walk_number(walk_sampling):
 
 
 def test_legal_combinations_time_crossover_edges(walk_sampling):
-
     walk_sampling.process_new_edge(1, 2, time.time())
     walk_sampling.process_new_edge(0, 1, time.time())
 
@@ -40,7 +39,6 @@ def test_legal_combinations_time_crossover_edges(walk_sampling):
 
 
 def test_legal_combinations_time_linear_edges(walk_sampling):
-
     walk_sampling.process_new_edge(0, 1, time.time())
     walk_sampling.process_new_edge(1, 2, time.time())
     walks = walk_sampling.process_new_edge(2, 3, time.time())

python/tgn.py

@@ -94,6 +94,7 @@
 # params and classes
 ##################
 
+
 # params TGN must receive
 class TGNParameters:
     NUM_OF_LAYERS = "num_of_layers"
@@ -820,7 +821,6 @@ def train_eval_epochs(
     assert batch_size > 0
 
     for epoch in range(num_epochs):
-
         # update global epoch counter
         update_epoch_counter()
 
@@ -886,6 +886,7 @@ def train_eval_epochs(
 
 # all available read_procs
 
+
 #####################################################
 @mgp.read_proc
 def predict_link_score(
@@ -1287,6 +1288,7 @@ def is_correctly_typed(defined_types, input_values):
 
 # helper functions
 
+
 #####################################
 def get_tgn_layer_enum(layer_type: str) -> TGNLayerType:
     if TGNLayerType(layer_type) is TGNLayerType.GraphAttentionEmbedding: